Chain Of Thought Prompting Under Streaming Batch: A Case Study

Yuxin Tang

Keywords: Large Language Model

Abstract:

Recently, Large Language Models (LLMs) have demonstrated remarkable capa-
bilities. Chain-of-Thought (CoT) has been proposed as a way of assisting LLMs
in performing complex reasoning. However, developing effective prompts can be
a challenging and labor-intensive task. Many studies come out of some way to au-
tomatically construct CoT from test data. Most of them assume that all test data is
visible before testing and only select a small subset to generate rationales, which
is an unrealistic assumption. In this paper, we present a case study on how to
construct and optimize chain-of-thought prompting using batch data in streaming
settings.

TL;DR: We present a case study on how to construct and optimize chain-of-thought prompting using batch data in streaming settings.